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Vibration characteristics of a structure can be used as an indication of its state of structural health as they vary if the structural health is affected by damage. This is the broad principle used in structural health monitoring for vibration-based damage detection of structures. Although most structures are built to have a long life span, they can incur damage due to many reasons. Early damage detection and appropriate retrofitting will enable the continued safe and efficient functioning of structures. This study develops and applies a dual-criteria method based on vibration characteristics to detect and locate damage in arch bridges. Steel arch bridges are one of the most aesthetically pleasing bridge types, which are reasonably popular in Australia and elsewhere. They exhibit three-dimensional and somewhat complex vibration characteristics that may not be suitable for traditional vibration-based damage detection methods. There have been relatively fewer studies on damage detection in these bridge types, and in particular the arch rib and struts, which are important structural components, have received little attention for damage detection. This study will address this research gap and treat the damage detection in the arch bridge structural components using the dual-criteria method to give unambiguous results. The proposed method is first validated by experimental data obtained from testing of a laboratory arch bridge model. The experimental results are also used to validate the modelling techniques and this is followed by damage detection studies on this bridge model as well as on a full-scale long-span arch bridge. Results demonstrate that the proposed dual-criteria method based on the two damage indices can detect and locate damage in the arch rib and vertical columns of deck-type arch bridges with considerable accuracy under a range of damage scenarios using only a few of the early modes of vibration. [ABSTRACT FROM AUTHOR]

This article introduces and evaluates the piecewise polynomial truncated singular value decomposition algorithm toward an effective use for moving force identification. Suffering from numerical non-uniqueness and noise disturbance, the moving force identification is known to be associated with ill-posedness. An important method for solving this problem is the truncated singular value decomposition algorithm, but the truncated small singular values removed by truncated singular value decomposition may contain some useful information. The piecewise polynomial truncated singular value decomposition algorithm extracts the useful responses from truncated small singular values and superposes it into the solution of truncated singular value decomposition, which can be useful in moving force identification. In this article, a comprehensive numerical simulation is set up to evaluate piecewise polynomial truncated singular value decomposition, and compare this technique against truncated singular value decomposition and singular value decomposition. Numerically simulated data are processed to validate the novel method, which show that regularization matrix L and truncating point k are the two most important governing factors affecting identification accuracy and ill-posedness immunity of piecewise polynomial truncated singular value decomposition. [ABSTRACT FROM AUTHOR]

Damage identification for complex structures is a challenging task due to the large amount of structural elements, limited number of measured modes and uncertainties in referenced numerical models. This article presents a study on enhancing the effectiveness of modal characteristics correlation methods for damage identification of complex structures. First, a correlation method using change in the ratio of modal strain energy to eigenvalue is introduced. Damage information is determined via a forward approach by optimizing the correlation level between the patterns of the analytical and measured changes in the ratio of modal strain energy to eigenvalue. Different from traditional optimization-based forward methods that require accurate numerical models, damage sensitivity coefficients of the ratio of modal strain energy to eigenvalue are directly estimated from the experimental modal information. To enhance the damage identification capability, both the elemental modal strain energy–eigenvalue ratio and the total modal strain energy–eigenvalue ratio components are examined in the correlation function. Second, a sensitivity-weighted search space scheme incorporated with genetic algorithm is developed to overcome the ill-posed problem that causes false detection errors. Finally, the correlation method and the enhanced technique are experimentally tested on a complex truss model with nearly 100 elements. To deal with the huge number of degrees of freedom in this structure, a multi-layout roving test with the adoption of redundant channels is designed, and a three-criterion strategy is used for the selection of modes. Results demonstrate the effectiveness of the proposed damage assessment framework to locate and estimate damage in complex truss structures. [ABSTRACT FROM AUTHOR]

For assessment of existing bridges, load rating is usually performed to assess the capacity against vehicular loading. Codified load rating can be conservative if the rating is not coupled with the field data or if simplifications are incorporated into assessment. Recent changes made to the Australian Bridge assessment code (AS 5100.7) distinguish the difference between design and assessment requirements, and include addition of structural health monitoring for bridge assessment. However, very limited guidelines are provided regarding higher order assessment levels, where more refined approaches are required to optimize the accuracy of the assessment procedure. This article proposes a multi-tier assessment procedure for capacity estimation of existing bridges using a combination of structural health monitoring techniques, advanced nonlinear analysis, and probabilistic approaches to effectively address the safety issues on aging bridges. Assessment of a Box Girder bridge was carried out according to the proposed multi-tier assessment, using data obtained from modal and destructive testing. Results of analysis at different assessment tiers showed that both load-carrying capacity and safety index of the bridge vary significantly if current bridge information is used instead of as-designed bridge information. Findings emerged from this study demonstrated that accuracy of bridge assessment is significantly improved when structural health monitoring techniques along with reliability approaches and nonlinear finite element analysis are incorporated, which will have important implications that are relevant to both practitioners and asset managers. [ABSTRACT FROM AUTHOR]

In recent years, it has become increasingly important to develop methodologies for reliable deterioration assessment of civil structures over their life cycle to facilitate maintenance and/or rehabilitation planning processes. Several approaches have been established to address this issue mainly using Bayesian probabilistic model updating techniques with some capability to incorporate uncertainties in the updating process. However, Bayesian model updating techniques are often found to be complex and computationally inefficient as opposed to their deterministic counterparts such as conventional or hybrid techniques of sensitivity-based model updating. Nevertheless, the deterministic model updating techniques have not been well developed for sophisticated assessment applications such as deterioration evaluation. To address these issues, this article presents a novel methodology for deterioration assessment of building structures under serviceability loading conditions, based upon an improved hybrid model updating approach incorporating the use of long-term monitoring data. This is first realized by a simple but effective scheme to simulate the deterioration mechanism in serviceability loading conditions before enhanced with innovative solutions to classify structural elements as well as to handle measurement and updating uncertainties in a meaningful way. The effectiveness of the established methodology is illustrated through a benchmark 10-story reinforced concrete building which is equipped with a long-term structural health monitoring system. [ABSTRACT FROM AUTHOR]

This article presents the field applications and validations for the controlled Monte Carlo data generation scheme. This scheme was previously derived to assist the Mahalanobis squared distance–based damage identification method to cope with data-shortage problems which often cause inadequate data multinormality and unreliable identification outcome. To do so, real-vibration datasets from two actual civil engineering structures with such data (and identification) problems are selected as the test objects which are then shown to be in need of enhancement to consolidate their conditions. By utilizing the robust probability measures of the data condition indices in controlled Monte Carlo data generation and statistical sensitivity analysis of the Mahalanobis squared distance computational system, well-conditioned synthetic data generated by an optimal controlled Monte Carlo data generation configurations can be unbiasedly evaluated against those generated by other set-ups and against the original data. The analysis results reconfirm that controlled Monte Carlo data generation is able to overcome the shortage of observations, improve the data multinormality and enhance the reliability of the Mahalanobis squared distance–based damage identification method particularly with respect to false-positive errors. The results also highlight the dynamic structure of controlled Monte Carlo data generation that makes this scheme well adaptive to any type of input data with any (original) distributional condition. [ABSTRACT FROM AUTHOR]

Structural damage detection using measured dynamic data for pattern recognition is a promising approach. These pattern recognition techniques utilize artificial neural networks and genetic algorithm to match pattern features. In this study, an artificial neural network–based damage detection method using frequency response functions is presented, which can effectively detect nonlinear damages for a given level of excitation. The main objective of this article is to present a feasible method for structural vibration–based health monitoring, which reduces the dimension of the initial frequency response function data and transforms it into new damage indices and employs artificial neural network method for detecting different levels of nonlinearity using recognized damage patterns from the proposed algorithm. Experimental data of the three-story bookshelf structure at Los Alamos National Laboratory are used to validate the proposed method. Results showed that the levels of nonlinear damages can be identified precisely by the developed artificial neural networks. Moreover, it is identified that artificial neural networks trained with summation frequency response functions give higher precise damage detection results compared to the accuracy of artificial neural networks trained with individual frequency response functions. The proposed method is therefore a promising tool for structural assessment in a real structure because it shows reliable results with experimental data for nonlinear damage detection which renders the frequency response function–based method convenient for structural health monitoring. [ABSTRACT FROM AUTHOR]

The use of Mahalanobis squared distance–based novelty detection in statistical damage identification has become increasingly popular in recent years. The merit of the Mahalanobis squared distance–based method is that it is simple and requires low computational effort to enable the use of a higher dimensional damage-sensitive feature, which is generally more sensitive to structural changes. Mahalanobis squared distance–based damage identification is also believed to be one of the most suitable methods for modern sensing systems such as wireless sensors. Although possessing such advantages, this method is rather strict with the input requirement as it assumes the training data to be multivariate normal, which is not always available particularly at an early monitoring stage. As a consequence, it may result in an ill-conditioned training model with erroneous novelty detection and damage identification outcomes. To date, there appears to be no study on how to systematically cope with such practical issues especially in the context of a statistical damage identification problem. To address this need, this article proposes a controlled data generation scheme, which is based upon the Monte Carlo simulation methodology with the addition of several controlling and evaluation tools to assess the condition of output data. By evaluating the convergence of the data condition indices, the proposed scheme is able to determine the optimal setups for the data generation process and subsequently avoid unnecessarily excessive data. The efficacy of this scheme is demonstrated via applications to a benchmark structure data in the field. [ABSTRACT FROM AUTHOR]

The study presents a multi-layer genetic algorithm (GA) approach using correlation-based methods to facilitate damage determination for through-truss bridge structures. To begin, the structure's damage-suspicious elements are divided into several groups. In the first GA layer, the damage is initially optimised for all groups using correlation objective function. In the second layer, the groups are combined to larger groups and the optimisation starts over at the normalised point of the first layer result. Then the identification process repeats until reaching the final layer where one group includes all structural elements and only minor optimisations are required to fine tune the final result. Several damage scenarios on a complicated through-truss bridge example are nominated to address the proposed approach's effectiveness. Structural modal strain energy has been employed as the variable vector in the correlation function for damage determination. Simulations and comparison with the traditional single-layer optimisation shows that the proposed approach is efficient and feasible for complicated truss bridge structures when the measurement noise is taken into account. [ABSTRACT FROM AUTHOR]

Columns and walls in buildings are subjected to a number of load increments during the construction and service stages. The combination of these load increments and poor quality construction can cause defects in these structural components. In addition, defects can also occur due to accidental or deliberate actions by users of the building during construction and service stages. Such defects should be detected early so that remedial measures can be taken to improve life time serviceability and performance of the building. This paper uses micro and macro model upgrading methods during construction and service stages of a building based on the mass and stiffness changes to develop a comprehensive procedure for locating and detecting defects in columns and walls of buildings. Capabilities of the procedure are illustrated through examples. [ABSTRACT FROM AUTHOR]

Acoustic emission (AE) is the phenomenon where stress waves are generated due to rapid release of energy within a material caused by sources such as crack initiation or growth. AE technique involves recording the stress waves by means of sensors and subsequent analysis of the recorded signals to gather information about the nature of the source. Though AE technique is one of the popular non destructive evaluation (NDE) techniques for structural health monitoring of mechanical, aerospace and civil structures; several challenges still exist in successful application of this technique. Presence of spurious noise signals can mask genuine damage-related AE signals; hence a major challenge identified is finding ways to discriminate signals from different sources. Analysis of parameters of recorded AE signals, comparison of amplitudes of AE wave modes and investigation of uniqueness of recorded AE signals have been mentioned as possible criteria for source differentiation. This paper reviews common approaches currently in use for source discrimination, particularly focusing on structural health monitoring of civil engineering structural components such as beams; and further investigates the applications of some of these methods by analyzing AE data from laboratory tests. [ABSTRACT FROM AUTHOR]

Bridge managers all over the world are always looking for simple ways to measure bridge vertical displacements for structural health monitoring. However, traditional methods to obtain such data are either tedious or expensive. There is a need to develop a simple, inexpensive, and yet practical method to measure bridge vertical displacements. This paper proposes two methods using either optical fiber (FBG) sensors or a charge-coupled-device (CCD) camera, respectively, for vertical displacement measurements of bridges. The FBG sensor method is based on the measured horizontal strains together with the identified curvature functions obtained by a self-developed FBG Tilt sensor. CCD cameras use a large number of pixels to form an image. The CCD camera method utilizes image processing techniques for pixel identification and subsequent edge detection. A preliminary study to validate the proposed methods in laboratory was presented. The tests include applying the methods to determine the vertical displacements separately for a concrete beam and a steel beam under various loadings. The comparisons include their installations, costs, degrees of accuracy, external factors affecting the measurement, etc. It was concluded that both methods could be used for vertical displacement measurement, and they could be complementary with one another. It was suggested to further improve the two methods developed and a successful outcome will not only help to solve an important problem for bridge management, but also prepare the way for better structural health monitoring techniques. [ABSTRACT FROM AUTHOR]

Reports on the validation of the Kaufman Functional Academic Skills Test (K-FAST) and Kaufman Short Neuropsychological Assessment Procedure (K-SNAP) cognitive measures against the short form of the Wechsler Adult Intelligence Scale-Revised (WAIS-R). Sample of patients hospitalized with depression; Findings.

A preface to the journal "Advances in Structural Engineering" is presented.